SSSD is a system daemon. Its primary function is to provide access to identity and authentication remote resource through a common framework that can provide caching and offline support to the system. It provides PAM and NSS modules, and in the future will support D-BUS based interfaces for extended user information. It also provides a better database to store local users as well as extended user data.

DING-LIBS (DING Is Not Glib) are a set of small, useful libraries that the System Security Services Daemon (SSSD) uses and makes available to other projects. They include: libdhash, an implementation of a dynamic hash table which will dynamically resize to achieve optimal storage and access time properties; ini_config, a library for parsing and managing INI files; path_utils, a library to manage UNIX paths and subsets of paths; collection, a generic, hierarchical grouping mechanism for complex data sets; ref_array, a dynamically-growing, reference-counted array; libbasicobjects, a set of fundamental object types for C.

This package provides an R wrapper around the fast T-distributed Stochastic Neighbor Embedding using a Barnes-Hut implementation.

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

This is a package to provide infrastructure for managing package parameters. Parameters are easy to get in relevant functions within a package, and rrror is thrown if a parameter is missing. Developers are able to register parameters and set their default value in a config file that is part of the package in YAML format, and users are able to override parameters using their own YAML. Users get an exception when trying to override a parameter that was not registered, and can load multiple parameters to the current environment.

dcor is distance correlation and energy statistics in Python.

E-statistics are functions of distances between statistical observations in metric spaces. Distance covariance and distance correlation are dependency measures between random vectors introduced in [SRB07] with a simple E-statistic estimator.

This package offers functions for calculating several E-statistics such as:

• estimator of the energy distance [SR13]

• biased and unbiased estimators of distance covariance and distance correlation [SRB07]

• estimators of the partial distance covariance and partial distance covariance [SR14]

Given a regression model, segmented updates the model by adding one or more segmented (i.e., piecewise-linear) relationships. Several variables with multiple breakpoints are allowed.

This package provides a small wrapper on regexpr to extract the matches and captured groups from the match of a regular expression to a character vector.

emcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC).

The trimmed k-means clustering method by Cuesta-Albertos, Gordaliza and Matran (1997). This optimizes the k-means criterion under trimming a portion of the points.

This package provides a toolbox for working with base types, core R features like the condition system, and core Tidyverse features like tidy evaluation.

MAPIE allows you to easily estimate prediction intervals (or prediction sets) using your favourite scikit-learn-compatible model for single-output regression or multi-class classification settings.

Prediction intervals output by MAPIE encompass both aleatoric and epistemic uncertainties and are backed by strong theoretical guarantees thanks to conformal prediction methods intervals.

Chaospy is a numerical toolbox for performing uncertainty quantification using polynomial chaos expansions, advanced Monte Carlo methods implemented in Python. It also include a full suite of tools for doing low-discrepancy sampling, quadrature creation, polynomial manipulations, and a lot more.

This package provides utility functions useful when programming and developing R packages.

This package provides functions to read flat or tabular text files from disk (or a connection).

Mixedpower uses pilotdata and a linear mixed model fitted with lme4 to simulate new data sets. Power is computed separate for every effect in the model output as the relation of significant simulations to all simulations. More conservative simulations as a protection against a bias in the pilotdata are available as well as methods for plotting the results.

Command-line tool and C library for reading files from popular stats packages like SAS, Stata and SPSS.

George is a fast and flexible Python library for Gaussian Process (GP) Regression, focused on efficiently evaluating the marginalized likelihood of a dataset under a GP prior, even as this dataset gets Big.

rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.

XLISP-STAT is a statistical environment based on a Lisp dialect called XLISP. To facilitate statistical computations, standard functions for addition, logarithms, etc., have been modified to operate on lists and arrays of numbers, and a number of basic statistical functions have been added. Many of these functions have been written in Lisp, and additional functions can be added easily by a user. Several basic forms of plots, including histograms, scatterplots, rotatable plots and scatterplot matrices are provided. These plots support various forms of interactive highlighting operations and can be linked so points highlighted in one plot will be highlighted in all linked plots. Interactions with the plots are controlled by the mouse, menus and dialog boxes. An object-oriented programming system is used to allow menus, dialogs, and the response to mouse actions to be customized.

This package provides methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4.

This package provides a resampling-based inference based on data resampling and permutation.

Features:

• Bootstrap resampling: ordinary or balanced with optional stratification

• Extended bootstrap resampling: also varies sample size

• Parametric resampling: Gaussian, Poisson, gamma, etc.)

• Jackknife estimates of bias and variance of any estimator

• Compute bootstrap confidence intervals (percentile or BCa) for any estimator

• Permutation-based variants of traditional statistical tests (USP test of independence and others)

• Tools for working with empirical distributions (CDF, quantile, etc.)

This package is a model building aid for nonlinear mixed-effects (population) model analysis using NONMEM, facilitating data set checkout, exploration and visualization, model diagnostics, candidate covariate identification and model comparison. The methods are described in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) <doi:10.1016/s0169-2607(98)00067-4>.

This package performs KDE operations on multidimensional data to calculate estimated PDFs (probability distribution functions), and resample new data from those PDFs.